
"""simple docstring"""




import unittest

from datasets import load_dataset

from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers

from ...test_tokenization_common import TokenizerTesterMixin

@require_tokenizers
class 						a__		( _A,     unittest.TestCase		):
   __lowerCAmelCase				=							None
   __lowerCAmelCase				=							BloomTokenizerFast
   __lowerCAmelCase				=							BloomTokenizerFast
   __lowerCAmelCase				=							True
   __lowerCAmelCase				=							False
   __lowerCAmelCase				=							"""tokenizer_file"""
   __lowerCAmelCase				=							{"""bos_token""": """<s>""", """eos_token""": """</s>""", """unk_token""": """<unk>""", """pad_token""": """<pad>"""}
   def      __magic_name__     (     self							):
          super().setUp()
          lowercase       :							Union[str, Any]									=				BloomTokenizerFast.from_pretrained("bigscience/tokenizer"							)
          tokenizer.save_pretrained(self.tmpdirname							)
   def      __magic_name__     (     self      ,       **_a							):
          kwargs.update(self.special_tokens_map							)
          return BloomTokenizerFast.from_pretrained(self.tmpdirname      ,       **snake_case_							)
   def      __magic_name__     (     self							):
          lowercase       :							List[Any]									=				self.get_rust_tokenizer()

          lowercase       :							int									=				["""The quick brown fox</s>""", """jumps over the lazy dog</s>"""]
          lowercase       :							Tuple									=				[[2_175, 23_714, 73_173, 144_252, 2], [77, 132_619, 3_478, 368, 109_586, 35_433, 2]]

          lowercase       :							Optional[int]									=				tokenizer.batch_encode_plus(snake_case_							)["""input_ids"""]
          self.assertListEqual(snake_case_      ,       snake_case_							)

          lowercase       :							List[str]									=				tokenizer.batch_decode(snake_case_							)
          self.assertListEqual(snake_case_      ,       snake_case_							)
   def      __magic_name__     (     self      ,       _a=6							):
          for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
                 with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})"""							):
                        lowercase       :							Any									=				self.rust_tokenizer_class.from_pretrained(snake_case_      ,       **snake_case_							)
                        # tokenizer_r.pad_token = None # Hotfixing padding = None
                        # Simple input
                        lowercase       :							Any									=				"""This is a simple input"""
                        lowercase       :							Optional[Any]									=				["""This is a simple input 1""", """This is a simple input 2"""]
                        lowercase       :							int									=				("""This is a simple input""", """This is a pair""")
                        lowercase       :							Dict									=				[
                            ("""This is a simple input 1""", """This is a simple input 2"""),
                            ("""This is a simple pair 1""", """This is a simple pair 2"""),
                        ]

                        # Simple input tests
                        try:
                               tokenizer_r.encode(snake_case_      ,       max_length=snake_case_							)
                               tokenizer_r.encode_plus(snake_case_      ,       max_length=snake_case_							)

                               tokenizer_r.batch_encode_plus(snake_case_      ,       max_length=snake_case_							)
                               tokenizer_r.encode(snake_case_      ,       max_length=snake_case_							)
                               tokenizer_r.batch_encode_plus(snake_case_      ,       max_length=snake_case_							)
                        except ValueError:
                               self.fail("Bloom Tokenizer should be able to deal with padding"							)

                        lowercase       :							Any									=				None  # Hotfixing padding = None
                        self.assertRaises(snake_case_      ,       tokenizer_r.encode      ,       snake_case_      ,       max_length=snake_case_      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(snake_case_      ,       tokenizer_r.encode_plus      ,       snake_case_      ,       max_length=snake_case_      ,       padding="max_length"							)

                        # Simple input
                        self.assertRaises(
                            snake_case_      ,       tokenizer_r.batch_encode_plus      ,       snake_case_      ,       max_length=snake_case_      ,       padding="max_length"      ,       )

                        # Pair input
                        self.assertRaises(snake_case_      ,       tokenizer_r.encode      ,       snake_case_      ,       max_length=snake_case_      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(snake_case_      ,       tokenizer_r.encode_plus      ,       snake_case_      ,       max_length=snake_case_      ,       padding="max_length"							)

                        # Pair input
                        self.assertRaises(
                            snake_case_      ,       tokenizer_r.batch_encode_plus      ,       snake_case_      ,       max_length=snake_case_      ,       padding="max_length"      ,       )
   def      __magic_name__     (     self							):
          lowercase       :							Dict									=				self.get_rust_tokenizer()
          lowercase       :							Union[str, Any]									=				load_dataset("xnli"      ,       "all_languages"      ,       split="test"      ,       streaming=snake_case_							)

          lowercase       :							Any									=				next(iter(snake_case_							)							)["""premise"""]  # pick up one data
          lowercase       :							Tuple									=				list(sample_data.values()							)

          lowercase       :							int									=				list(map(tokenizer.encode      ,       snake_case_							)							)
          lowercase       :							str									=				[tokenizer.decode(snake_case_      ,       clean_up_tokenization_spaces=snake_case_							) for x in output_tokens]
          self.assertListEqual(snake_case_      ,       snake_case_							)

   def      __magic_name__     (     self							):
          # The test has to be overriden because BLOOM uses ALiBi positional embeddings that does not have
          # any sequence length constraints. This test of the parent class will fail since it relies on the
          # maximum sequence length of the positoonal embeddings.
          self.assertGreaterEqual(len(self.tokenizer_class.pretrained_vocab_files_map							)      ,       1							)
          self.assertGreaterEqual(len(list(self.tokenizer_class.pretrained_vocab_files_map.values()							)[0]							)      ,       1							)




import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table import array_cast
from ..utils.py_utils import no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							from .features import FeatureType


lowerCamelCase_ ,							lowerCamelCase_ ,							lowerCamelCase_    :			Dict    =	False, False, False

@dataclass
class 							_UpperCamelCase					:






			'''simple docstring'''

			__UpperCamelCase   :    Optional[int]          =			None
			__UpperCamelCase   :    bool          =			True
			__UpperCamelCase   :    bool          =			True
			__UpperCamelCase   :    Optional[str]          =			None
			# Automatically constructed
			__UpperCamelCase   :    ClassVar[str]          =			"dict"
			__UpperCamelCase   :    ClassVar[Any]          =			pa.struct({"""bytes""": pa.binary(), """path""": pa.string()}	)
			__UpperCamelCase   :    str          =			field(default="""Audio"""  ,							init=_A  ,							repr=_A	)

			def __call__(							self			:							List[Any]   ):
							return self.pa_type

			def    lowerCAmelCase__						(							self			:							Optional[int]    ,						snake_case_			:							Union[str, bytes, dict]   ):
							try:
											import soundfile as sf  # soundfile is a dependency of librosa, needed to decode audio files.
							except ImportError as err:
											raise ImportError("""To support encoding audio data, please install 'soundfile'."""   ) from err
							if isinstance(snake_case_    ,						snake_case_   ):
											return {"bytes": None, "path": value}
							elif isinstance(snake_case_    ,						snake_case_   ):
											return {"bytes": value, "path": None}
							elif "array" in value:
											# convert the audio array to wav bytes
											UpperCamelCase_:				List[Any]						=    BytesIO()
											sf.write(snake_case_    ,						value["""array"""]    ,						value["""sampling_rate"""]    ,						format="""wav"""   )
											return {"bytes": buffer.getvalue(), "path": None}
							elif value.get("""path"""   ) is not None and os.path.isfile(value["""path"""]   ):
											# we set "bytes": None to not duplicate the data if they're already available locally
											if value["path"].endswith("""pcm"""   ):
															# "PCM" only has raw audio bytes
															if value.get("""sampling_rate"""   ) is None:
																			# At least, If you want to convert "PCM-byte" to "WAV-byte", you have to know sampling rate
																			raise KeyError("""To use PCM files, please specify a 'sampling_rate' in Audio object"""   )
															if value.get("""bytes"""   ):
																			# If we already had PCM-byte, we don`t have to make "read file, make bytes" (just use it!)
																			UpperCamelCase_:				str						=    np.frombuffer(value["""bytes"""]    ,						dtype=np.intaa   ).astype(np.floataa   ) / 3_2767
															else:
																			UpperCamelCase_:				Dict						=    np.memmap(value["""path"""]    ,						dtype="""h"""    ,						mode="""r"""   ).astype(np.floataa   ) / 3_2767

															UpperCamelCase_:				Dict						=    BytesIO(bytes()   )
															sf.write(snake_case_    ,						snake_case_    ,						value["""sampling_rate"""]    ,						format="""wav"""   )
															return {"bytes": buffer.getvalue(), "path": None}
											else:
															return {"bytes": None, "path": value.get("""path"""   )}
							elif value.get("""bytes"""   ) is not None or value.get("""path"""   ) is not None:
											# store the audio bytes, and path is used to infer the audio format using the file extension
											return {"bytes": value.get("""bytes"""   ), "path": value.get("""path"""   )}
							else:
											raise ValueError(
											    f'''An audio sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''   )

			def    lowerCAmelCase__						(							self			:							List[str]    ,						snake_case_			:							dict    ,						snake_case_			:							Optional[Dict[str, Union[str, bool, None]]] = None   ):
							if not self.decode:
											raise RuntimeError("""Decoding is disabled for this feature. Please use Audio(decode=True) instead."""   )

							UpperCamelCase_, UpperCamelCase_:				Any						=    (value["""path"""], BytesIO(value["""bytes"""]   )) if value["""bytes"""] is not None else (value["""path"""], None)
							if path is None and file is None:
											raise ValueError(f'''An audio sample should have one of \'path\' or \'bytes\' but both are None in {value}.'''   )

							try:
											import librosa
											import soundfile as sf
							except ImportError as err:
											raise ImportError("""To support decoding audio files, please install 'librosa' and 'soundfile'."""   ) from err

							UpperCamelCase_:				Optional[Any]						=    xsplitext(snake_case_   )[1][1:].lower() if path is not None else None
							if not config.IS_OPUS_SUPPORTED and audio_format == "opus":
											raise RuntimeError(
											    """Decoding 'opus' files requires system library 'libsndfile'>=1.0.31, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """   )
							elif not config.IS_MP3_SUPPORTED and audio_format == "mp3":
											raise RuntimeError(
											    """Decoding 'mp3' files requires system library 'libsndfile'>=1.1.0, """
											    """You can try to update `soundfile` python library: `pip install \"soundfile>=0.12.1\"`. """   )

							if file is None:
											UpperCamelCase_:				Dict						=    token_per_repo_id or {}
											UpperCamelCase_:				str						=    path.split("""::"""   )[-1]
											try:
															UpperCamelCase_:				Any						=    string_to_dict(snake_case_    ,						config.HUB_DATASETS_URL   )["""repo_id"""]
															UpperCamelCase_:				List[str]						=    token_per_repo_id[repo_id]
											except (ValueError, KeyError):
															UpperCamelCase_:				Any						=    None

											with xopen(snake_case_    ,						"""rb"""    ,						use_auth_token=snake_case_   ) as f:
															UpperCamelCase_, UpperCamelCase_:				List[Any]						=    sf.read(snake_case_   )

							else:
											UpperCamelCase_, UpperCamelCase_:				Union[str, Any]						=    sf.read(snake_case_   )

							UpperCamelCase_:				List[str]						=    array.T
							if self.mono:
											UpperCamelCase_:				int						=    librosa.to_mono(snake_case_   )
							if self.sampling_rate and self.sampling_rate != sampling_rate:
											UpperCamelCase_:				Union[str, Any]						=    librosa.resample(snake_case_    ,						orig_sr=snake_case_    ,						target_sr=self.sampling_rate   )
											UpperCamelCase_:				Dict						=    self.sampling_rate

							return {"path": path, "array": array, "sampling_rate": sampling_rate}

			def    lowerCAmelCase__						(							self			:							Tuple   ):
							from .features import Value

							if self.decode:
											raise ValueError("""Cannot flatten a decoded Audio feature."""   )
							return {
							    "bytes": Value("""binary"""   ),
							    "path": Value("""string"""   ),
							}

			def    lowerCAmelCase__						(							self			:							Any    ,						snake_case_			:							Union[pa.StringArray, pa.StructArray]   ):
							if pa.types.is_string(storage.type   ):
											UpperCamelCase_:				Optional[Any]						=    pa.array([None] * len(snake_case_   )    ,						type=pa.binary()   )
											UpperCamelCase_:				List[str]						=    pa.StructArray.from_arrays([bytes_array, storage]    ,						["""bytes""", """path"""]    ,						mask=storage.is_null()   )
							elif pa.types.is_binary(storage.type   ):
											UpperCamelCase_:				str						=    pa.array([None] * len(snake_case_   )    ,						type=pa.string()   )
											UpperCamelCase_:				List[Any]						=    pa.StructArray.from_arrays([storage, path_array]    ,						["""bytes""", """path"""]    ,						mask=storage.is_null()   )
							elif pa.types.is_struct(storage.type   ) and storage.type.get_all_field_indices("""array"""   ):
											UpperCamelCase_:				Union[str, Any]						=    pa.array([Audio().encode_example(snake_case_   ) if x is not None else None for x in storage.to_pylist()]   )
							elif pa.types.is_struct(storage.type   ):
											if storage.type.get_field_index("""bytes"""   ) >= 0:
															UpperCamelCase_:				Optional[Any]						=    storage.field("""bytes"""   )
											else:
															UpperCamelCase_:				str						=    pa.array([None] * len(snake_case_   )    ,						type=pa.binary()   )
											if storage.type.get_field_index("""path"""   ) >= 0:
															UpperCamelCase_:				List[str]						=    storage.field("""path"""   )
											else:
															UpperCamelCase_:				int						=    pa.array([None] * len(snake_case_   )    ,						type=pa.string()   )
											UpperCamelCase_:				Dict						=    pa.StructArray.from_arrays([bytes_array, path_array]    ,						["""bytes""", """path"""]    ,						mask=storage.is_null()   )
							return array_cast(snake_case_    ,						self.pa_type   )




			def    lowerCAmelCase__						(							self			:							Optional[Any]    ,						snake_case_			:							pa.StructArray   ):

							@no_op_if_value_is_null
							def path_to_bytes(snake_case_			:							Optional[int]   ):
											with xopen(snake_case_    ,						"""rb"""   ) as f:
															UpperCamelCase_:				Optional[int]						=    f.read()
											return bytes_

							UpperCamelCase_:				int						=    pa.array(
							    [
							        (path_to_bytes(x["""path"""]   ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
							        for x in storage.to_pylist()
							    ]    ,						type=pa.binary()    ,						)
							UpperCamelCase_:				Union[str, Any]						=    pa.array(
							    [os.path.basename(snake_case_   ) if path is not None else None for path in storage.field("""path"""   ).to_pylist()]    ,						type=pa.string()    ,						)
							UpperCamelCase_:				Optional[int]						=    pa.StructArray.from_arrays([bytes_array, path_array]    ,						["""bytes""", """path"""]    ,						mask=bytes_array.is_null()   )
							return array_cast(snake_case_    ,						self.pa_type   )






